Pattern Analysis Computation Methods and Algorithms for Machine Learning

In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.
Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do “fuzzy” matching of inputs. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output to the sort provided by pattern-recognition algorithms.

 

Pattern Analysis Computation Methods:

  • Ridge regression
  • Regularized Fisher discriminant
  • Regularized kernel Fisher discriminant
  • Maximizing variance
  • Maximizing covariance
  • Canonical correlation analysis
  • Kernel CCA
  • Regularized CCA
  • Kernel regularized CCA
  • Smallest enclosing hyper sphere
  • Soft minimal hyper sphere
  • nu-soft minimal hyper sphere
  • Hard margin SVM
  • 1-norm soft margin SVM
  • 2-norm soft margin SVM
  • Ridge regression optimization
  • Quadratic e-insensitive
  • Linear e-insensitive SVR
  • nu-SVR 
  • Soft ranking 
  • Cluster quality 
  • Cluster optimization strategy 
  • Multiclass clustering
  • Relaxed multiclass clustering 
  • Visualization quality

Pattern Analysis Algorithms:

  • Normalization 
  • Centering data 
  • Simple novelty detection 
  • Parzen based classifier 
  • Cholesky decomposition or dual Gram�Schmidt 
  • Standardizing data 
  • Kernel Fisher discriminant 
  • Primal PCA 
  • Kernel PCA 
  • Whitening 
  • Primal CCA 
  • Kernel CCA 
  • Principal components regression 
  • PLS feature extraction 
  • Primal PLS 
  • Kernel PLS 
  • Smallest hyper sphere enclosing data 
  • Soft hyper sphere minimization 
  • nu-soft minimal hyper sphere 
  • Hard margin SVM 
  • Alternative hard margin SVM 
  • 1-norm soft margin SVM 
  • nu-SVM
  • 2-norm soft margin SVM
  • Kernel ridge regression
  • 2-norm SVR
  • 1-norm SVR
  • nu-support vector regression
  • Kernel perceptron
  • Kernel adatron 
  • On-line SVR
  • nu-ranking
  • On-line ranking
  • Kernel k-means
  • MDS for kernel-embedded data
  • Data visualization

Source: http://www.kernel-methods.net/algos.html

Keywords: Data Mining, Machine Learning, Algorithms

 

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